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Existing techniques for unionable table search define unionability usingmetadata (tables must have the same or similar schemas) or column-based metrics(for example, the values in a table should be drawn from the same domain). Inthis work, we introduce the use of semantic relationships between pairs ofcolumns in a table to improve the accuracy of union search. Consequently, weintroduce a new notion of unionability that considers relationships betweencolumns, together with the semantics of columns, in a principled way. To do so,we present two new methods to discover semantic relationship between pairs ofcolumns. The first uses an existing knowledge base (KB), the second (which wecall a "synthesized KB") uses knowledge from the data lake itself. We adopt anexisting Table Union Search benchmark and present new (open) benchmarks thatrepresent small and large real data lakes. We show that our new unionabilitysearch algorithm, called SANTOS, outperforms a state-of-the-art union searchthat uses a wide variety of column-based semantics, including word embeddingsand regular expressions. We show empirically that our synthesized KB improvesthe accuracy of union search by representing relationship semantics that maynot be contained in an available KB. This result hints at a promising future ofcreating a synthesized KBs from data lakes with limited KB coverage and usingthem for union search.

SANTOS: Relationship-based Semantic Table Union Search